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| import PyPDF2 | |
| import nltk | |
| from nltk.tokenize import sent_tokenize | |
| import random | |
| import requests | |
| import streamlit as st | |
| from langchain_openai import OpenAI | |
| # Download NLTK data (if not already downloaded) | |
| nltk.download('punkt') | |
| nltk.download('averaged_perceptron_tagger') | |
| # ChatGPT API endpoint | |
| CHATGPT_API_ENDPOINT = "https://api.openai.com/v1/chat/completions" | |
| OPENAI_API_KEY = "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" | |
| def extract_text_from_pdf(pdf_file): | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page_num in range(len(pdf_reader.pages)): | |
| text += pdf_reader.pages[page_num].extract_text() | |
| return text | |
| def generate_mcqs_on_topic(text, topic, num_mcqs=5): | |
| # Tokenize the text into sentences | |
| sentences = nltk.sent_tokenize(text) | |
| # Randomly select sentences to create Questions | |
| selected_sentences = random.sample(sentences, min(num_mcqs, len(sentences))) | |
| mcqs = [] | |
| for sentence in selected_sentences: | |
| # Use ChatGPT for interactive question generation | |
| chatgpt_question = generate_question_with_chatgpt(sentence) | |
| mcqs.append(chatgpt_question) | |
| return mcqs | |
| def generate_question_with_chatgpt(context): | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {OPENAI_API_KEY}", | |
| } | |
| # Initializing the default value | |
| generated_question = { | |
| 'content': "Unable to generate a question..", | |
| 'options': [], # assuming options is a list | |
| 'correct_answer': "Unknown" | |
| } | |
| data = { | |
| "model": "gpt-3.5-turbo", | |
| "temperature": 0.7, | |
| "max_tokens": 1024, | |
| "messages": [ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"What is the question for the following? {context}"}, | |
| ], | |
| } | |
| response = requests.post(CHATGPT_API_ENDPOINT, json=data, headers=headers) | |
| result = response.json() | |
| print("API Response:", result) # Add this line for debugging | |
| if 'choices' in result: | |
| # Extract the generated question, options, and correct answer from the response | |
| generated_question = { | |
| 'content': result["choices"][0]["message"]["content"], | |
| 'options': result["choices"][0]["message"].get("options", []), | |
| 'correct_answer': result["choices"][0]["message"].get("correct_answer", "Unknown") | |
| } | |
| else: | |
| print("Unexpected API response format.") | |
| return generated_question | |
| def main(): | |
| # Title of the Application | |
| st.header("🤖CB Quiz Generator🧠", divider='rainbow') | |
| st.subheader("☕CoffeeBeans☕") | |
| # User input | |
| pdf_file = st.file_uploader("Upload PDF Document:", type=["pdf"]) | |
| num_mcqs = st.number_input("Enter Number of MCQs to Generate:", min_value=1, step=1, value=5) | |
| topic = st.text_input("Enter the Topic in which the quiz has to be generated") | |
| # Button to trigger QUIZ generation | |
| if st.button("Generate Quiz"): | |
| if pdf_file: | |
| text = extract_text_from_pdf(pdf_file) | |
| mcqs = generate_mcqs_on_topic(text, topic, num_mcqs) | |
| # Display the generated Questions | |
| st.success(f"Generated {num_mcqs} Questions:") | |
| for i, generated_question in enumerate(mcqs, start=1): | |
| st.write(f"\nQuestion {i}: {generated_question['content']}") | |
| st.write(f"Options: {', '.join(generated_question['options'])}") | |
| st.write(f"Correct Answer: {generated_question['correct_answer']}") | |
| else: | |
| st.error("Please upload a PDF document.") | |
| if __name__ == "__main__": | |
| main() | |